A deep learning-based comparative study to track mental depression from EEG data

Background: Modern day's society is engaged in commitment-based and time-bound jobs. This invites tension and mental depression among many people who are not able to cope up with this type of working environment. Cases of mental depression are increasing day by day all over the world. Recently, the onset of the COVID-19 pandemic has added further fuel to the fire. In many countries, the ratio between patients with mental depression and psychiatrists or psychologists is remarkably poor. Under such a situation, the design, and development of an expert system by exploiting the hidden power of various deep learning (DL) and machine learning (ML) techniques can solve the problem up to a greater extent. Methodology: Each deep learning and machine learning technique has got its advantages and disadvantages to handle different classification problems. In this article four neural network-based deep learning architectures namely MLP, CNN, RNN, RNN with LSTM, and two Supervised Machine Learning Techniques such as SVM and LR are implemented to investigate and compare their suitability to track the mental depression from EEG Data. Result: Among Neural Network-Based Deep Learning techniques RNN model has achieved the highest accuracy with 97.50% in Training Set and 96.50% in the Testing set respectively. It has been followed with RNN with LSTM model when there were 40% data in the Testing Set. Whereas both the Supervised Machine Learning Models namely SVM and LR have outperformed with 100.00% accuracies in Training Phase and approximately 97.25% accuracies in Testing Phase respectively. Conclusion: This investigation and comparison-oriented study establish the suitability of RNN, RNN with LSTM, SVM and LR model to track mental depression from EEG data. This type of comparative research using Machine Learning and Deep learning architectures must be framed out on this topic to finalize the design and development of an expert system for the automatic detection of depression from EEG data.